Sep 2026· Canadian Journal of Marketing Research· Vol 16, pp. 583-590
Abstract
Artificial Intelligence (AI) is becoming increasingly usual in HR management, especially in talent acquisition and AI-assisted decision making. But, in addition to the technological capacities, the successful application of AI needs HR professionals to understand and evaluate the recommendations of AI. In the present research, the impact of AI literacy and Explainable Artificial Intelligence (XAI) on AI-augmented talent acquisition is inves-tigated. Quantitative analysis was performed using 410 valid observations of HR managers interacting with AI recruitment systems under various conditions of XAI. Subjective perception of AI systems and objective understanding of AI recommendations in relation to Important Features, Counterfactuals and Model Criteria explanations were considered. It has been established that all three XAI methods increase subjective percep-tion significantly. Nevertheless, there is a huge difference in the way they affect objective understanding. Important Features greatly increase objective understanding, Counterfactuals do not matter at all and Model Criteria lead to a degradation of objective under-standing. It was found out that there is a positive relationship between AI literacy and objective understanding. The results of the research demonstrate that there should be balance between AI literacy, explanation design and human judgement for effective AI augmented HRM.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post AI Has Grown Up. Have We? appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026